f10acddb78
Ascend scheduler was added for non chunk prefill case before, since that the npu ops didn't work well with chunked prefill. Now the ops with chunked prefill work better, it's time to remove the ascend scheduler to use vLLM default scheduler. - vLLM version: v0.11.2 --------- Signed-off-by: wangxiyuan <wangxiyuan1007@gmail.com>
90 lines
3.4 KiB
Python
90 lines
3.4 KiB
Python
#
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# Copyright (c) 2025 Huawei Technologies Co., Ltd. All Rights Reserved.
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# Copyright 2023 The vLLM team.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# This file is a part of the vllm-ascend project.
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# Adapted from vllm/tests/basic_correctness/test_basic_correctness.py
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#
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"""Compare the short outputs of HF and vLLM when using greedy sampling.
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Run `pytest tests/test_offline_inference.py`.
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"""
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from vllm import SamplingParams
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from vllm.assets.audio import AudioAsset
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from vllm.assets.image import ImageAsset
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from tests.e2e.conftest import VllmRunner
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def test_multimodal_vl(prompt_template):
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image = ImageAsset("cherry_blossom") \
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.pil_image.convert("RGB")
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img_questions = [
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"What is the content of this image?",
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"Describe the content of this image in detail.",
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"What's in the image?",
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"Where is this image taken?",
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]
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images = [image] * len(img_questions)
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prompts = prompt_template(img_questions)
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with VllmRunner("Qwen/Qwen2.5-VL-3B-Instruct",
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max_model_len=4096,
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mm_processor_kwargs={
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"min_pixels": 28 * 28,
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"max_pixels": 1280 * 28 * 28,
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"fps": 1,
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},
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enforce_eager=False) as vllm_model:
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outputs = vllm_model.generate_greedy(prompts=prompts,
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images=images,
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max_tokens=64)
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assert len(outputs) == len(prompts)
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for _, output_str in outputs:
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assert output_str, "Generated output should not be empty."
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def test_multimodal_audio():
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audio_prompt = "".join([
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f"Audio {idx+1}: <|audio_bos|><|AUDIO|><|audio_eos|>\n"
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for idx in range(2)
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])
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question = "What sport and what nursery rhyme are referenced?"
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prompt = ("<|im_start|>system\nYou are a helpful assistant.<|im_end|>\n"
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"<|im_start|>user\n"
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f"{audio_prompt}{question}<|im_end|>\n"
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"<|im_start|>assistant\n")
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mm_data = {
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"audio": [
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asset.audio_and_sample_rate for asset in
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[AudioAsset("mary_had_lamb"),
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AudioAsset("winning_call")]
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]
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}
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inputs = {"prompt": prompt, "multi_modal_data": mm_data}
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sampling_params = SamplingParams(temperature=0.2,
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max_tokens=10,
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stop_token_ids=None)
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with VllmRunner("Qwen/Qwen2-Audio-7B-Instruct",
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max_model_len=4096,
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max_num_seqs=5,
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dtype="bfloat16",
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limit_mm_per_prompt={"audio": 2},
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gpu_memory_utilization=0.9) as runner:
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outputs = runner.generate(inputs, sampling_params=sampling_params)
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assert outputs is not None, "Generated outputs should not be None."
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assert len(outputs) > 0, "Generated outputs should not be empty."
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